Suboptimality analysis of receding horizon predictive control with terminal constraints

Geng, Xiaojun; Xi, Yugeng

doi:10.1016/s1474-6670(17)56509-x

Cited by 2 publications

(1 citation statement)

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“…Gurobi or CPLEX) may fail to find that optimal solution due to rounding errors and built-in tolerances [27]. Moreover, the receding horizon formulation of Problem 3, introduced for the sake of computational tractability, results in suboptimality [36], [37]. Due to these factors, no strong theoretical guarantee can be given regarding the optimality of the MILP stage.…”

Section: ) Collision Avoidancementioning

confidence: 99%

A Two-Stage Optimization-Based Motion Planner for Safe Urban Driving

Eiras

Hawasly²,

Albrecht

et al. 2022

IEEE Trans. Robot.

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Recent road trials have shown that guaranteeing the safety of driving decisions is essential for the wider adoption of autonomous vehicle technology. One promising direction is to pose safety requirements as planning constraints in nonlinear, non-convex optimization problems of motion synthesis. However, many implementations of this approach are limited by uncertain convergence and local optimality of the solutions achieved, affecting overall robustness. To improve upon these issues, we propose a novel two-stage optimization framework: in the first stage, we find a solution to a Mixed-Integer Linear Programming (MILP) formulation of the motion synthesis problem, the output of which initializes a second Nonlinear Programming (NLP) stage. The MILP stage enforces hard constraints of safety and road rule compliance generating a solution in the right subspace, while the NLP stage refines the solution within the safety bounds for feasibility and smoothness. We demonstrate the effectiveness of our framework via simulated experiments of complex urban driving scenarios, outperforming a state-of-the-art baseline in metrics of convergence, comfort and progress.

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Section: ) Collision Avoidancementioning

confidence: 99%